TDP_human_MERFISH

1. Pre-analysis Overview

1.1 Submission Form

1.2 MERFISH Data Quality

The summaries present the data quality assessment automatically generated by MERSCOPE for each experiment. We mainly focus on the transcripts level for each sample. So we’re looking for high density in transcripts, based on the transcripts count per field of view (FOV), transcript density in FOV, and frequency of transcripts detected.

Generally, log10 transcript count > 4.0 for human brain sample in most FOC can be considered as a good quality standard.

Need to note that the low accuracy in DAPI cell boundary is not a concern, as a self-designed cell segmentation processing will take over this task.

1.3 MERFISH Sample Index

Sample Information
Batch Sample Index Genotype Data Path
1 H250 Control_1 Control Y:/Lab/MERFISH_Imaging_data_2/202407221453_20240722Maize28H250BP1738h271x01_VMSC00101/region_0
1 MN28 ALS_1 MN Y:/Lab/MERFISH_Imaging_data_2/202407221453_20240722Maize28H250BP1738h271x01_VMSC00101/region_1
2 H260 Control_2 Control Y:/Lab/MERFISH_Imaging_data_2/202407221100_20240722105620240722Maize13H260xBP1738x2_VMSC05201/region_0
2 MN13 ALS_2 MN Y:/Lab/MERFISH_Imaging_data_2/202407221100_20240722105620240722Maize13H260xBP1738x2_VMSC05201/region_1
3 H264 Control_3 Control Y:/Lab/MERFISH_Imaging_data_2/202407261252_20240726MaizeMN32H264BP1738x03_VMSC00101/region_0
3 MN32 ALS_3 MN Y:/Lab/MERFISH_Imaging_data_2/202407261252_20240726MaizeMN32H264BP1738x03_VMSC00101/region_1
4 H262 Control_4 Control Y:/Lab/MERFISH_Imaging_data_2/202408051335_20240805MaizeMN40H262BP1738x05_VMSC00101/region_0
4 MN40 ALS_4 MN Y:/Lab/MERFISH_Imaging_data_2/202408051335_20240805MaizeMN40H262BP1738x05_VMSC00101/region_1

1.3.1 H250, (Control_1)

1.3.2 MN28, (ALS_1)

1.3.3 H260, (Control_2)

1.3.4 MN13, (ALS_2)

1.3.5 H264, (Control_3)

1.3.6 MN32, (ALS_3)

1.3.7 H262, (Control_4)

1.3.8 MN40, (ALS_4)

1.3.9 , H246 ( Failure Sample!)

1.3.10 , MN31 ( Failure Sample!)

2. Data Processing

2.1 Cell Segmentation

Based on the spatial information and images obtained from MERFISH, we developed a machine learning model using the Cellpose algorithm to distinguish individual cells via MERFISH DAPI images.

To ensure the data quality and accuracy of cells, we have defined the minimum and maximum values for cell volume and gene count per cell. The cell volume should be between [100, 2500].

2.2 Transcript level

2.3 Cell Annotation

We performed cell annotation by applying Leiden clustering to all cells across all samples. This clustering method subgrouped the cells based on their gene expression profiles. Then, for each identified cluster, we defined their cell types by matching the differential expression gene with known cell type markers.

2.3.1 Cell Type Umap

2.3.2 Cell Count Table

Annotated Cell Count Table
id Astrocytes Endo.Pericytes GABAergic Glutamatergic Microglia Oligo Total
ALS_1 903 575 534 4475 191 3904 10582
ALS_2 51 8 230 1712 15 216 2232
ALS_3 964 535 1629 5299 107 4476 13010
ALS_4 6915 2169 1808 4081 3566 19352 37891
Control_1 409 337 445 1938 50 4303 7482
Control_2 33 43 578 2296 35 1075 4060
Control_3 1724 531 1814 4895 191 1841 10996
Control_4 1235 492 0 1 0 1211 2939
Total 12234 4690 7038 24697 4155 36378 89192

2.3.3 Cell Type Spatial Map

3. ALS Gene

A list of ALS (Amyotrophic Lateral Sclerosis) Gene is provided by Maize. We applied these gene on MERFISH spatial map to observe the expression pattern between different conditions and regions.

3.1 ANXA11

3.2 ATXN1

3.3 ATXN2

3.4 C9orf72

3.5 CAMTA1

3.6 CFAP410

3.7 CHCHD10

3.8 ENAH

3.9 EPHA4

3.10 FUS

3.11 HFE

3.12 HNRNPA1

3.13 KIF5A

3.14 NEK1

3.15 NIPA1

3.16 OPTN

3.17 PFN1

3.18 SCFD1

3.19 SOD1

3.20 TARDBP

3.21 TBK1

3.22 TUBA4A

3.23 UBQLN2

3.24 UNC13A

3.25 VAPB

3.26 VCP

4. Differentiation Anlaysis

We used Wilcoxon Test to compute gene differential expression (DE). The statistical significance was cut-off by log2(Fold Change) > 1 or log2(Fold Change) < -1 and p_value < 0.05.

4.1 Glutamatergic

4.1.1 Glutamatergic DE Volcano

4.1.2 Glutamatergic DE Table

gene logfoldchanges pvals pvals_adj
APOE 4.852753 1.615e-235 4.376e-233
SLC1A3 3.067187 2.707e-34 5.644e-33
AGT 3.034761 1.360e-104 1.842e-102
GJA1 2.970548 2.168e-51 7.343e-50
MAG 2.636984 4.546e-62 2.464e-60
DNM1 2.460739 1.451e-28 2.314e-27
EPAS1 2.315546 3.088e-29 5.578e-28
COL6A2 2.252038 5.702e-03 1.776e-02
KIF5A 2.220677 7.571e-82 6.839e-80
AQP4 2.190669 1.370e-06 6.513e-06
IRAG1 1.942560 4.552e-20 4.935e-19
PBXIP1 1.886192 2.182e-81 1.478e-79
GAD1 1.831045 1.236e-15 1.196e-14
SLC1A2 1.766074 8.365e-29 1.417e-27
CLDN5 1.735199 7.181e-06 3.243e-05
ID4 1.686231 1.360e-07 6.955e-07
SLCO2B1 1.451002 2.912e-09 1.754e-08
ZNF536 1.352059 3.352e-03 1.095e-02
ACSS1 1.265586 5.960e-14 5.384e-13
SLC6A1 1.241011 5.520e-05 2.338e-04
PPP2CB 1.212883 2.360e-11 1.729e-10
SRRM2 1.205891 9.516e-37 2.149e-35
TTYH2 1.182761 2.117e-06 9.723e-06
ARX 1.155217 2.831e-05 1.218e-04
CSF1R 1.041076 2.339e-10 1.625e-09
MYRF 1.032829 1.195e-17 1.245e-16

4.2 GABAergic

4.2.1 GABAergic DE Volcano

4.2.2 GABAergic DE Table

gene logfoldchanges pvals pvals_adj
APOE 4.979272 1.515e-156 4.105e-154
AQP4 3.992170 2.686e-04 1.462e-03
SLC1A3 3.761728 2.285e-19 4.423e-18
GJA1 3.467355 1.503e-25 4.073e-24
MAG 3.402290 2.973e-36 1.343e-34
SNAP25 2.808171 1.762e-07 1.592e-06
DNM1 2.800718 1.345e-12 1.822e-11
AGT 2.769075 1.314e-48 1.187e-46
EPAS1 2.529825 1.596e-16 2.704e-15
SLC1A2 2.502425 7.202e-21 1.626e-19
SLCO2B1 2.453097 1.091e-04 6.426e-04
PBXIP1 2.434515 1.112e-41 7.534e-40
SLC17A7 2.401027 2.122e-20 4.424e-19
ID4 2.358978 8.619e-04 4.027e-03
IRAG1 2.318151 2.904e-07 2.539e-06
COL27A1 2.098511 1.767e-06 1.409e-05
SRRM2 2.097627 1.425e-58 1.931e-56
SYNJ2 2.022911 3.216e-05 2.141e-04
MOG 2.016278 6.655e-08 6.441e-07
KIF5A 1.993070 2.002e-38 1.085e-36
MYRF 1.830010 4.929e-12 6.072e-11
ITPR2 1.805335 5.959e-05 3.670e-04
ERMN 1.792674 3.479e-11 3.929e-10
MOBP 1.595363 7.230e-03 3.015e-02
TRHDE 1.576332 2.846e-10 3.085e-09
CSF1R 1.569159 3.390e-04 1.801e-03
TTYH2 1.560506 9.715e-05 5.850e-04
PHAX 1.500887 2.073e-03 9.424e-03
UPF3A 1.342266 7.464e-04 3.612e-03
ZNF469 1.342215 1.833e-26 5.520e-25
ACSS1 1.312243 4.672e-10 4.870e-09
HNRNPU 1.193893 1.336e-18 2.414e-17
CACNA1A 1.128119 9.458e-33 3.662e-31
PPP2CB 1.019995 6.003e-04 3.013e-03